Devices for detecting direct mind-machine interaction (DMMI) have been proposed and researched for many years. The most carefully controlled and best-explored experiments utilize some type of true, or non-deterministic, random number generator (TRNG) that produces a sequence of random numbers, usually in a binary form. The most common random number generators used are of the electronic type that produce a sequence of random binary bits, or events. This has been referred to as a random event generator (REG).
U.S. Pat. No. 6,369,727, issued Apr. 9, 2002, to Vincze, teaches a random number generator using an analog-to-digital converter to convert random noise into digital samples that are transformed by a reductive mapping into uniformly distributed random numbers for output. U.S. Pat. No. 6,581,078, issued Jun. 17, 2003, to Liardet, teaches a random number generator in which a physical voice source produces digital signals that are combined with signals produced by a pseudo-random number generator. U.S. Pat. No. 4,853,884, issued Aug. 1, 1989, to Brown et al, teaches a random number generator in which a zener diode produces a random binary number output having a controlled degree of randomness determined in response to an input control signal.
In typical DMMI experiments, an REG is operated in conjunction with a human operator who attempts to influence the statistical properties of the REG's output sequence. The operator, or subject, is directed to intend mentally the number of ones produced in the random sequence to be either higher, lower, or equal to the statistically expected number.
The results of these experiments, compiled over thousands of experimental trials, show a small but persistent and statistically significant effect. A most notable example of a research program for detecting DMMI is the long-standing program at Princeton University, known as Princeton Engineering Anomalies Research (PEAR). This work is described in detail in the book Margins of Reality, the Role of Mind in the Physical World, by Robert Jahn and Brenda Dunne, Harcourt Brace and Company, 1987.
The PEAR lab and numerous other facilities around the world have established, to a very high level of statistical significance, the existence of a link between the mental intention of an operator and results of measurements of REG output. Demonstrating the reality of DMMI is of great scientific interest. However, the laboratory demonstration has not translated into useful devices or methods. Practical applications of DMMI have not previously been achieved due to an absence of understanding of why or how the effect manifests, and because the experimental devices and data processing methods used are not sensitive enough to the effect.
Journal articles by many authors have suggested a variety of potential uses of DMMI. These suggestions are made without disclosing means for their implementation. Apparatuses for experiments involving DMMI have been complex and expensive. U.S. Pat. No. 5,830,064, issued Nov. 3, 1998, to Bradish et al, teaches a method and apparatus of generating values and detecting whether the values fall outside chance expectations. This patent involves converting some of the values according to a selection pattern in order to measure a collective statistical variance.
There are, in fact, no practical devices currently on the market that utilize DMMI. This is because all previous devices and methods have required intense and extended effort to produce even a single correct bit of information.